library(tidyverse)
## ── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(maps)
## 
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
## 
##     map
library(mapdata)
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(viridis)
## Loading required package: viridisLite
library(wesanderson)
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") 
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_character(),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
    borders("world", colour = NA, fill = "grey90") +
    theme_bw() +
    geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
    labs(title = 'World COVID-19 Confirmed cases',x = '', y = '',
        size="Cases (x1000))") +
    theme(legend.position = "right") +
    coord_fixed(ratio=1.5)
## Warning: Removed 54 rows containing missing values (geom_point).

daily_report <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-05-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Country_Region == "US") %>% 
  filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
                  "Puerto Rico","Northern Mariana Islands", 
                  "Virgin Islands", "Recovered", "Guam", "Grand Princess",
                  "District of Columbia", "Diamond Princess")) %>% 
  filter(Lat > 0)
## Parsed with column specification:
## cols(
##   FIPS = col_character(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
    borders("state", colour = "black", fill = "grey90") +
    theme_bw() +
    geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
    labs(title = 'COVID-19 Confirmed Cases in the US', x = '', y = '',
        size="Cases (x1000))") +
    theme(legend.position = "right") +
    coord_fixed(ratio=1.5)

mybreaks <- c(1, 100, 1000, 10000, 10000)
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed)) +
    borders("state", colour = "white", fill = "grey90") +
    geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
    scale_size_continuous(name="Cases", trans="log", range=c(1,7), 
                        breaks=mybreaks, labels = c("1-99",
                        "100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
    scale_color_viridis_c(option="viridis",name="Cases",
                        trans="log", breaks=mybreaks, labels = c("1-99",
                        "100-999", "1,000-9,999", "10,000-99,999", "50,000+"))  +
# Cleaning up the graph
  
  theme_void() + 
    guides( colour = guide_legend()) +
    labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US'") +
    theme(
      legend.position = "bottom",
      text = element_text(color = "#22211d"),
      plot.background = element_rect(fill = "#ffffff", color = NA), 
      panel.background = element_rect(fill = "#ffffff", color = NA), 
      legend.background = element_rect(fill = "#ffffff", color = NA)
    ) +
    coord_fixed(ratio=1.5)
## Warning: Transformation introduced infinite values in discrete y-axis

## Warning: Transformation introduced infinite values in discrete y-axis
## Warning in sqrt(x): NaNs produced
## Warning: Removed 40 rows containing missing values (geom_point).

daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Country_Region == "US") %>% 
  group_by(Province_State) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Province_State = tolower(Province_State))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_character(),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
# load the US map data
us <- map_data("state")
# We need to join the us map data with our daily report to make one data frame/tibble
state_join <- left_join(us, daily_report, by = c("region" = "Province_State"))
# plot state map
# plot state map
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
  scale_fill_gradientn(colours = 
                         wes_palette("Zissou1", 100, type = "continuous"),
                         trans = "log10") +
  labs(title = "COVID-19 Confirmed Cases in the US'")

library(RColorBrewer)
# To display only colorblind-friendly brewer palettes, specify the option colorblindFriendly = TRUE as follow:
# display.brewer.all(colorblindFriendly = TRUE)
# Get and format the covid report data
report_03_27_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  unite(Key, Admin2, Province_State, sep = ".") %>% 
  group_by(Key) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Key = tolower(Key))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_character(),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
# dim(report_03_27_2020)
# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>% 
  unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, report_03_27_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  # Add data layer
  borders("state", colour = "black") +
  geom_polygon(data = state_join, aes(fill = Confirmed)) +
  scale_fill_gradientn(colors = brewer.pal(n = 5, name = "PuRd"),
                       breaks = c(1, 10, 100, 1000, 10000, 100000),
                       trans = "log10", na.value = "White") +
  ggtitle("Number of Confirmed Cases by US County") +
  theme_bw() 
## Warning: Transformation introduced infinite values in discrete y-axis

daily_report <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Province_State == "Massachusetts") %>% 
  group_by(Admin2) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Admin2 = tolower(Admin2))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_character(),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
us <- map_data("state")
ma_us <- subset(us, region == "massachusetts")
counties <- map_data("county")
ma_county <- subset(counties, region == "massachusetts")
state_join <- left_join(ma_county, daily_report, by = c("subregion" = "Admin2")) 
# plot state map
ggplot(data = ma_county, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "white") +
    scale_fill_gradientn(colors = brewer.pal(n = 5, name = "BuGn"),
                         trans = "log10") +
  labs(title = "COVID-19 Confirmed Cases in Massachusetts'")

daily_report
## # A tibble: 14 x 2
##    Admin2              Confirmed
##    <chr>                   <dbl>
##  1 barnstable                283
##  2 berkshire                 213
##  3 bristol                   424
##  4 dukes and nantucket        12
##  5 essex                    1039
##  6 franklin                   85
##  7 hampden                   546
##  8 hampshire                 102
##  9 middlesex                1870
## 10 norfolk                   938
## 11 plymouth                  621
## 12 suffolk                  1896
## 13 unassigned                270
## 14 worcester                 667
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly(
  ggplot(data = ma_county, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
    scale_fill_gradientn(colours = 
                         wes_palette("Zissou1", 100, type = "continuous")) +
  ggtitle("COVID-19 Cases in MA") +
# Cleaning up the graph
  labs(x=NULL, y=NULL) +
  theme(panel.border = element_blank()) +
  theme(panel.background = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(axis.text = element_blank())
)
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

##Exercise 1

##For the above graph “World COVID-19 Confirmed case” summarize the counts for each Country on the graph and update the graph to 9/26/2020. You may need to adjust the size of the points.

Confirmed_World_09_26 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
  rename(Long = "Long_") 
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
Confirmed_World_09_26 %>% 
  group_by(Country_Region) %>% 
  summarize(Confirmed = sum(Confirmed), Deaths = sum(Deaths), min_deaths = min(Deaths)) %>% 
arrange(desc(min_deaths)) 
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 188 x 4
##    Country_Region Confirmed Deaths min_deaths
##    <chr>              <dbl>  <dbl>      <dbl>
##  1 US               7078089 204490     204490
##  2 Brazil           4717991 141406     141406
##  3 India            5903932  93379      93379
##  4 Mexico            726431  76243      76243
##  5 United Kingdom    431817  42060      42060
##  6 Italy             308104  35818      35818
##  7 Peru              794584  32037      32037
##  8 France            552454  31675      31675
##  9 Spain             716481  31232      31232
## 10 Iran              443086  25394      25394
## # … with 178 more rows
ggplot(Confirmed_World_09_26, aes(x = Long, y = Lat, size = Confirmed/1000)) +
    borders("world", colour = NA, fill = "grey90") +
    theme_bw() +
    geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
    labs(title = 'World COVID-19 Confirmed cases',x = '', y = '',
        size="Cases (x1000))") +
    theme(legend.position = "right") +
    coord_fixed(ratio=1.5)
## Warning: Removed 81 rows containing missing values (geom_point).

##Exercise 2

#Update Anisa Dhana’s graph layout of the US to 9/26/2020. You may need to adjust the size of the points.

Confirmed_State_09_26 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Country_Region == "US") %>% 
  filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
                  "Puerto Rico","Northern Mariana Islands", 
                  "Virgin Islands", "Recovered", "Guam", "Grand Princess",
                  "District of Columbia", "Diamond Princess")) %>% 
  filter(Lat > 0)
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
mybreaks <- c(1, 100, 1000, 10000, 10000)
ggplot(Confirmed_State_09_26, aes(x = Long, y = Lat, size = Confirmed)) +
    borders("state", colour = "white", fill = "grey90") +
    geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
    scale_size_continuous(name="Cases", trans="log", range=c(0,3), 
                        breaks=mybreaks, labels = c("1-99",
                        "100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
    scale_color_viridis_c(option="viridis",name="Cases",
                        trans="log", breaks=mybreaks, labels = c("1-99",
                        "100-999", "1,000-9,999", "10,000-99,999", "50,000+"))  +
# Cleaning up the graph
  
  theme_void() + 
    guides( colour = guide_legend()) +
    labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US'") +
    theme(
      legend.position = "bottom",
      text = element_text(color = "#22211d"),
      plot.background = element_rect(fill = "#ffffff", color = NA), 
      panel.background = element_rect(fill = "#ffffff", color = NA), 
      legend.background = element_rect(fill = "#ffffff", color = NA)
    ) +
    coord_fixed(ratio=1.5)
## Warning: Transformation introduced infinite values in discrete y-axis

## Warning: Transformation introduced infinite values in discrete y-axis
## Warning in sqrt(x): NaNs produced
## Warning: Removed 6 rows containing missing values (geom_point).

##Exercise 3

#Update the above graph “Number of Confirmed Cases by US County” to 9/26/2020 and use a different color scheme or theme

library(RColorBrewer)
# To display only colorblind-friendly brewer palettes, specify the option colorblindFriendly = TRUE as follow:
# display.brewer.all(colorblindFriendly = TRUE)
# Get and format the covid report data
report_09_26_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  unite(Key, Admin2, Province_State, sep = ".") %>% 
  group_by(Key) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Key = tolower(Key))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
# dim(report_03_27_2020)
# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>% 
  unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, report_09_26_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  # Add data layer
  borders("state", colour = "black") +
  geom_polygon(data = state_join, aes(fill = Confirmed)) +
  scale_fill_gradientn(colors = brewer.pal(n = 5, name = "GnBu"),
                       breaks = c(1, 10, 100, 1000, 10000, 100000),
                       trans = "log10", na.value = "White") +
  ggtitle("Number of Confirmed Cases by US County") +
  theme_bw()
## Warning: Transformation introduced infinite values in discrete y-axis

display.brewer.all(colorblindFriendly = TRUE)

##Just used this to see what the different color schemes were; technically part of Ex3

##Exercise 4

#Make an interactive plot using a state of your choosing using a theme different from used in the above examples.

daily_report_09_26_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
   rename(Long = "Long_") %>% 
  filter(Province_State == "Pennsylvania") %>% 
  group_by(Admin2) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Admin2 = tolower(Admin2))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
us <- map_data("state")
ma_us <- subset(us, region == "pennsylvania")
counties <- map_data("county")
pa_county <- subset(counties, region == "pennsylvania")
state_join <- left_join(pa_county, daily_report_09_26_2020, by = c("subregion" = "Admin2")) 
# plot state map
ggplot(data = pa_county, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "white") +
    scale_fill_gradientn(colors = brewer.pal(n = 5, name = "BuPu"),
                         trans = "log10") +
  labs(title = "COVID-19 Confirmed Cases in Pennsylvania'")

daily_report_09_26_2020
## # A tibble: 68 x 2
##    Admin2    Confirmed
##    <chr>         <dbl>
##  1 adams           824
##  2 allegheny     12101
##  3 armstrong       431
##  4 beaver         1878
##  5 bedford         238
##  6 berks          7022
##  7 blair           667
##  8 bradford        139
##  9 bucks          8760
## 10 butler         1045
## # … with 58 more rows
library(plotly)
ggplotly(
  ggplot(data = pa_county, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
    scale_fill_gradientn(colours = 
                         wes_palette("GrandBudapest2", 100, type = "continuous")) +
  ggtitle("COVID-19 Cases in PA") +
# Cleaning up the graph
  labs(x=NULL, y=NULL) +
  theme(panel.border = element_blank()) +
  theme(panel.background = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(axis.text = element_blank())
)